Environmental problems are on the rise, and climate change, global warming, and rapidly rising pollution are consistently topping the charts. We believe everyone knows about it by now, and efforts are already being poured in the direction of bringing the situation under control. But do you know besides humans, even the technologies like Artificial Intelligence and Machine Learning are playing crucial roles in this regard?
Today, through this blog post, we at the Tribhuvan College of Environment and Developmental Sciences, ranked among the renowned environmental science colleges in Delhi NCR, would like to draw your attention to the role of AI in making sustainability a sweet reality. Numbers suggest that the market size of AI in Sustainability, which was valued at USD 14.6 Bn in 2023, is likely to reach USD 56.9 Bn by 2031. Now that’s a whopping 19.1% CAGR in itself! So, let us see how technologies like AI and Machine Learning (as a subset of AI) can significantly contribute to driving meaningful environmental solutions. Read on.
How Does Machine Learning Contribute to Environmental Well-being?
The basis of Machine Learning revolves around training computers in data patterns to make predictions without the need for explicit programming. With this, we can leverage a large amount of data using machine learning algorithms. This eases out the process of identifying complex relationships, digging insights, and automating decision-making processes.
With respect to the environment, Machine Learning also helps in analysing environmental data, forecasting future trends, and refining processes to minimise environmental impacts.
From this, it becomes clear that these technologies do have a substantial role to play in ensuring environmental sustainability. Let us further see how.
The Role of Machine Learning in Curbing Climate Change:
WHO has parted with some disturbing statistics, which hold climate change responsible for an additional 25,000 deaths every year. This has made fighting climate change increasingly important, which is where we can take assistance from Machine Learning.
At a very basic level, it helps to use Machine Learning techniques such as Advanced Modelling and Predictive Analytics to help scientists and policymakers better understand the climate systems and develop effective strategies to reduce greenhouse gas emissions.
Climate modelling and forecasting involves using Machine Learning to analyse vast datasets collected from satellites, weather stations, and ocean buoys. This improves the accuracy of weather predictions, making tracking of climate patterns and future climate scenario-related forecasts more streamlined. Moreover, it ensures preparedness to deal with extreme weather conditions.
Machine Learning also makes real-time carbon emissions monitoring and management possible. A good example here is that of industries that use Machine Learning algorithms to optimise energy consumption by predicting energy demand and adjusting operations accordingly.
Smart grids are other achievements in this regard, which leverage Machine Learning to balance electric supply and demand, integrating renewable energy sources and reducing fossil fuel reliance.
Machine Learning in Improving the Output & Consumption of Renewable Energy:
Speaking of a sustainable future, we can seldom overlook the contribution of renewable energy. With this, we at the Tribhuvan College of Environment and Developmental Sciences would like you to know how important Machine Learning is in the production, distribution, and consumption of renewable resources.
In terms of optimising solar and wind power, Machine Learning helps to analyse weather patterns and historical data to predict solar and wind energy output. In the process, energy providers are able to optimise grid operations, reduce energy wastage, and improve reliance on renewable sources of energy.
Likewise, Predictive Analysis powered by Machine Learning helps reduce downtime for renewable energy infrastructure, thus improving operational efficiency.
Machine Learning in Biodiversity Conservation and Circular Economy:
The significance of Machine Learning moves further to help with wildlife monitoring and habitat protection. By bringing image recognition and acoustics into play, Machine Learning helps track wildlife populations to draw conclusions about endangered species. Similarly, automated monitoring systems help to analyse camera trap images and audio recordings to detect and classify animals based on real-time insights.
Furthermore, Machine Learning goes a mile ahead in identifying high-risk areas so that necessary environmental regulations can be enforced and fragile ecosystems protected.
We at the Tribhuvan College of Environment and Developmental Sciences have time and again spoken about the importance of achieving a circular economy. Here, too, Machine Learning helps with smart waste sorting by processing large volumes of waste with higher accuracy and speed. Likewise, Machine Learning also helps identify opportunities for material reuse and product lifecycle optimisation.
In a nutshell:
With everything we have discussed in this blog post, it is clear that Machine Learning is a leading force in coming up with relevant environment-pro solutions. No doubt, Machine Learning is a go-to as far as coming up with and executing innovative environmental solutions across industries is concerned.
Moreover, Machine Learning is helping redefine the very way we approach sustainability and bring the same into reality. Be it with respect to building sustainable supply chains or reducing the overall carbon footprint, Machine Learning makes all this and more possible by setting sustainability targets, tracking progress, and mitigating related risks.
Hence, we at the Tribhuvan College of Environment and Developmental Sciences, one of the renowned environmental science colleges in Delhi NCR, advocate for the responsible use of AI and Machine Learning to drive impactful change. The question now is—how will we leverage these innovations to protect our planet? The answer lies in our collective commitment to sustainable progress. Let’s embrace AI as a powerful ally in building a better tomorrow.